Introduction

Climate change is defined as the long term shifts in world temperature and weather patterns. The purpose of this project is to see the different greenhouse gases and climate indicators and the relationship that they have with climate change. We focus on different countries and identify their greenhouse gas emissions to determine which nations are the greatest contributors to the worsening climate crisis.

Climate change is a very real threat to human wellbeing. The changes in our landscapes will accelaratingly affect the wellbeing of many populations through higher instances of natural disasters such as drought or sea level rise. Most of the vulnerable populations are in nations that have historically emitted the least.

For our project, we’re interested in bringing this inequality of current and future well-being to light by looking at changes in climate change indicators such as temperature and freqency of natural disasters and national well-being indicators such as life expectancy and GDP.

Climate Indicators

Changes throughout time

We thought the well-documented change that our cognitive dissonance of continuing to go with our day-to-day lives while knowing this accelerating threat to Earth’s slow adapting creatures’ well-being refuses to fully acknowledge would be a great start.

In the graph above there is a clear increase in temperature from 1980 till 2015, which exhibits a clear correlation with the rise of Co2 Emissions worsening the temperature increases.. When examining average, maximum and minimum temperatures there is a 1 degree celsius increase in the temperatures. We believe this may surpass the 1.5 degree celsius increase in temperatures before 2025, that were promised by country representatives at the Paris Agreement

ggplotly(g)

Emissions and Temperature

Historically the high emitters are plotted in the graphs below. We explore countries that emit both carbon dioxide and nitrous oxide. We focus on the year 2015, as the data for the following years had many missing values. There is a clear trend as China, USA and India are high emitters of the main greenhouse gasses, and due to the size of the countries, economy and population there is a clear contribution made by these economics powers in worsening the climate crisis. There is a clear correlation between high temperatures and high emission of greenhouse gasses for most countries, probably leading to increased frequency of climate related disasters in those countries. ### Temperatures and CO2 emissions in 2015

t1

Mean Temperatures and Nitrous Oxide

x

Climate Disasters

CO2 Emitter Rankings Worldwide

The map below shows 10 of the top and bottom CO2 emitters for they year the 2015. We can see that most of the highest emitters are economic powerhouses such as China and United States, and the lower emitters tend to mostly be island nations. We can also find the life expectancy and GDP for each country.

#plotting on world map
leaflet(top10_bottom10) %>%
  addTiles() %>%
  addCircleMarkers(lng = ~longitude, lat = ~latitude, group = "Country",
                   fillColor=color_avail, stroke = TRUE, fillOpacity = 1, popup = popup_content)%>%
  addLegend(pal = pal, values = ~top10_bottom10$rank, title = "CO2 Emitters")

GHG Emissions

Carbon dioxide Emissions from 2010 till 2015

The plot below shows the exact levels of CO2 emissions of the top 10 emitters summed up from years 2010 to 2015. We can see a significant difference between the top three emitters, with China being significantly above US, and the US being significantly above India, then the emissions for each country slowly stalling off. Thus, there seems to be an acceleration to emission per each country given driving factors that aren’t included in this map, however could be attributed to current and historic economic growth levels per capita.

p1

Country Wellbeing Plots

Arable Land for Top and Bottom CO2 Emitters

Arable Land for Top and Bottom 10 CO2 Emitters The plot below shows countries that have less arable land tend to have emitted lower emissions except for China which despite its massive land area, they likely have forest and mountainous regions that cover most of the landmass. United States is the only country that seems to be balanced with being in the middle of the extreme values for both variables.

plot1

Arable Land and Percentage of Forested Land

In the plot below we can explore the realtionship between arable and forested lands and we can see that countries with higher forested land have lower arable lands.

ggplotly(arableland_gdp)

Economic and Population Wellbeing and CO2 Emissions

Co2 emissions + GDP (how top 10 and bottom 10 GDP countries compare in CO2 emissions from 2010-2015)

Below is a plot of the top and bottom CO2 Emitters worldwide.

plot3

GDP of countries Worldwide in 2015

The map below shows the top 10 countries based on GDP. This is another form of understanding of the wellbeing of countries, assuming higher GDP countries’ citizens have improved wellbeing. We can recognise most of the names from higher CO2 emitters as well such as countries like US and China.

plot5

The Map below shows the rural and urban population percentages alongside the GDP per capita. The graph is coloured according to GDP per capita which is a direct indicator of a countries economic well being. We link this to rural and urban populations to see whether there is a connection.

leaflet(world_map1)%>%
 addPolygons(stroke = TRUE, smoothFactor = 0.5,
  weight=1, color='#333333', opacity=1, 
  fillColor = ~colorQuantile("Reds", avg_gdp_cap)(avg_gdp_cap), 
  fillOpacity = 1,
  highlightOptions = highlightOptions(
    weight = 3,
    fillOpacity = 0.7,
    bringToFront = TRUE),
  popup = labels)

Deaths in Countries Worldwide in 2015

plot6

Focus on Vital Indicators

The map below shows the relationship between average temperature, average GDP, total disasters and total death count. There is a clear correlation between lower average GDP and higher temperatures and higher death count. The line on the plot shows to us that higher GDP nations have lower temperature, reinforcing the fact that the negative impact of climate is distributed amongst the world, whereas the benefits may not be.

ggplotly(s)

Thoughts and Insights

Data Collection in different countries differ so how valuable is all the data when comparing to developing nations and developed. There is a clear inequity between emitters and the impacted nations, so having more valuable data would help us delve into this deeper.